Superset is a code editor and terminal environment designed for the AI era. It allows developers to orchestrate multiple AI coding agents like Claude Code, Codex, and Gemini in parallel. It solves the problem of managing concurrent AI-assisted coding tasks by providing isolated Git worktrees, preventing merge conflicts, and enabling seamless switching between different agents and IDEs.
Free
How to use Superset?
Install Superset and launch it from your terminal. Create a new workspace to set up a parallel environment. You can then run commands to spawn multiple AI coding agents (like Claude Code or Cursor) on different tasks simultaneously. Each agent operates in its own isolated Git worktree. You can review code changes side-by-side within the interface and open any worktree in your preferred external IDE (VS Code, Cursor, JetBrains) with a single click.
Superset 's Core Features
Orchestrate multiple AI coding agents (Claude Code, Codex, Gemini, Cursor) in parallel, allowing you to work on features, bug fixes, and refactoring simultaneously.
Provides universal compatibility with any CLI-based AI agent, making it an agent-agnostic platform for seamless switching between different coding assistants.
Ensures complete isolation for each agent using dedicated Git worktrees, eliminating merge conflicts and allowing independent code review and merging.
Enables one-click opening of any worktree in your favorite external IDE, including VS Code, Cursor, Xcode, Sublime Text, or JetBrains products.
Features a built-in terminal and interface to monitor all active agents, review code changes in a diff view, and manage ongoing parallel coding sessions.
Maintains persistent sessions that survive laptop lid closures, allowing long-running AI agent tasks to continue uninterrupted.
Superset 's Use Cases
Solo developers can manage multiple AI agents to tackle different parts of a project concurrently, dramatically increasing coding speed and project throughput.
Engineering teams can use it to prototype features in parallel, with each agent working on a separate component, enabling rapid experimentation and iteration.
Open-source maintainers can delegate bug fixes, documentation updates, and feature implementations to different AI agents, reviewing all changes in one place.
Students and learners can run coding exercises with multiple AI tutors simultaneously, comparing different approaches and solutions from various AI models.
Startup founders with limited technical resources can leverage a swarm of AI agents to build and iterate on MVPs faster than a traditional development cycle.
